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  {
   "source": [
    "**异常检测实战task：**  \n",
    "1、基于 anomaly_data.csv数据，可视化数据分布情况、及其对应高斯分布的概率密度函数  \n",
    "2、建立模型，实现异常数据点预测  \n",
    "3、可视化异常检测处理结果  \n",
    "4、修改概率分布阈值EllipticEnvelope(contamination=0.1)中的contamination，查看阈值改变对结果的影响  "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "         x1         x2\n0  8.046815   9.741152\n1  8.408520   8.763270\n2  9.195915  10.853181\n3  9.914701  11.174260\n4  8.576700   9.042849",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x1</th>\n      <th>x2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>8.046815</td>\n      <td>9.741152</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8.408520</td>\n      <td>8.763270</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.195915</td>\n      <td>10.853181</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>9.914701</td>\n      <td>11.174260</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8.576700</td>\n      <td>9.042849</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('anomaly_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 赋值 X1, X2\n",
    "X1 = data.loc[:, 'x1']\n",
    "X2 = data.loc[:, 'x2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化数据\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "plt.scatter(X1, X2)\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.title('data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x360 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 将数据分布进行可视化\n",
    "fig1 = plt.figure(figsize=(20, 5))\n",
    "plt.subplot(121)\n",
    "# 100个数据分隔，即100个柱状图\n",
    "plt.hist(X1, bins=100)\n",
    "plt.title('x1 distribution statistics')\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('count')\n",
    "plt.subplot(122)\n",
    "# 100个数据分隔，即100个柱状图\n",
    "plt.hist(X2, bins=100)\n",
    "plt.title('x2 distribution statistics')\n",
    "plt.xlabel('x2')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "9.112225783931596 1.3559573758220915 9.997710507954396 1.3097071175386399\n"
    }
   ],
   "source": [
    "# 计算X1、X2的均值和标准差\n",
    "X1_mean = X1.mean()\n",
    "X1_sigma = X1.std()\n",
    "X2_mean = X2.mean()\n",
    "X2_sigma = X2.std()\n",
    "print(X1_mean, X1_sigma, X2_mean, X2_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[4.59439512e-11 6.39250512e-11 8.87272428e-11 1.22853079e-10\n 1.69690787e-10 2.33815685e-10 3.21389865e-10 4.40690686e-10\n 6.02807607e-10 8.22558389e-10 1.11969027e-09 1.52045032e-09\n 2.05963243e-09 2.78323813e-09 3.75192518e-09 5.04546423e-09\n 6.76848160e-09 9.05783765e-09 1.20920788e-08 1.61035107e-08\n 2.13935729e-08 2.83523613e-08 3.74833421e-08 4.94345445e-08\n 6.50378126e-08 8.53580478e-08 1.11754798e-07 1.45959055e-07\n 1.90168713e-07 2.47166865e-07 3.20467940e-07 4.14497664e-07\n 5.34813972e-07 6.88377332e-07 8.83880464e-07 1.13214921e-06\n 1.44662831e-06 1.84396812e-06 2.34473095e-06 2.97423845e-06\n 3.76358483e-06 4.75084425e-06 5.98250452e-06 7.51516355e-06\n 9.41752964e-06 1.17727715e-05 1.46812688e-05 1.82638203e-05\n 2.26653703e-05 2.80593218e-05 3.46525087e-05 4.26909039e-05\n 5.24661477e-05 6.43229811e-05 7.86676738e-05 9.59775394e-05\n 1.16811627e-04 1.41822682e-04 1.71770457e-04 2.07536462e-04\n 2.50140207e-04 3.00757010e-04 3.60737401e-04 4.31628133e-04\n 5.15194803e-04 6.13446032e-04 7.28659126e-04 8.63407096e-04\n 1.02058687e-03 1.20344847e-03 1.41562486e-03 1.66116213e-03\n 1.94454957e-03 2.27074923e-03 2.64522422e-03 3.07396532e-03\n 3.56351504e-03 4.12098835e-03 4.75408930e-03 5.47112255e-03\n 6.28099878e-03 7.19323310e-03 8.21793529e-03 9.36579093e-03\n 1.06480323e-02 1.20763980e-02 1.36630808e-02 1.54206617e-02\n 1.73620317e-02 1.95002975e-02 2.18486742e-02 2.44203621e-02\n 2.72284091e-02 3.02855590e-02 3.36040854e-02 3.71956133e-02\n 4.10709290e-02 4.52397803e-02 4.97106689e-02 5.44906370e-02\n 5.95850519e-02 6.49973899e-02 7.07290248e-02 7.67790232e-02\n 8.31439500e-02 8.98176902e-02 9.67912882e-02 1.04052811e-01\n 1.11587236e-01 1.19376374e-01 1.27398820e-01 1.35629945e-01\n 1.44041928e-01 1.52603826e-01 1.61281695e-01 1.70038746e-01\n 1.78835554e-01 1.87630310e-01 1.96379110e-01 2.05036290e-01\n 2.13554802e-01 2.21886617e-01 2.29983157e-01 2.37795764e-01\n 2.45276170e-01 2.52376989e-01 2.59052217e-01 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    }
   ],
   "source": [
    "# 计算高斯分布概率密度\n",
    "from scipy.stats import norm\n",
    "X1_range = np.linspace(0, 20, 300) # 范围0-20， 300个点均分\n",
    "X1_gauss = norm.pdf(X1_range, X1_mean, X1_sigma) # 计算高斯分布概率密度\n",
    "X2_range = np.linspace(0, 20, 300)\n",
    "X2_gauss = norm.pdf(X2_range, X2_mean, X2_sigma)\n",
    "print(X1_gauss, X2_gauss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化高斯分布概率密度\n",
    "fig3 = plt.figure(figsize=(20, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(X1_range, X1_gauss)\n",
    "plt.title('gauss p(X1)')\n",
    "plt.subplot(122)\n",
    "plt.plot(X2_range, X2_gauss)\n",
    "plt.title('gauss p(X2)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "EllipticEnvelope()"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# 建立模型，默认contamination=0.1\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "ad_model = EllipticEnvelope()\n",
    "ad_model.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1    276\n-1     31\ndtype: int64\n"
    }
   ],
   "source": [
    "# 预测\n",
    "y_predict = ad_model.predict(data)\n",
    "print(pd.value_counts(y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 可视化结果\n",
    "fig4 = plt.figure(figsize=(10, 5))\n",
    "orginal_data = plt.scatter(X1, X2, marker='x') # 各点用 x 表示\n",
    "#y_predict==-1即是异常点; marker='o'将异常点用圆圈圈起来; facecolor='none' 不填充，即空心圆; edgecolor='red' 颜色为红色; s=150 圆圈的大小\n",
    "anomaly_data = plt.scatter(X1[y_predict==-1], X2[y_predict==-1], marker='o', facecolor='none', edgecolor='red', s=150) \n",
    "plt.title('auto find anomaly data')\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.legend((orginal_data, anomaly_data), ('orginal_data', 'anomaly_data'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 修改阈值，contamination=0.01\n",
    "ad_model = EllipticEnvelope(contamination=0.025)\n",
    "ad_model.fit(data)\n",
    "y_predict = ad_model.predict(data)\n",
    "# 可视化结果\n",
    "fig5 = plt.figure(figsize=(10, 5))\n",
    "orginal_data = plt.scatter(X1, X2, marker='x')\n",
    "anomaly_data = plt.scatter(X1[y_predict==-1], X2[y_predict==-1], marker='o', facecolor='none', edgecolor='red', s=150) \n",
    "plt.title('auto find anomaly data')\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.legend((orginal_data, anomaly_data), ('orginal_data', 'anomaly_data'))\n",
    "plt.show()"
   ]
  }
 ]
}